Software & Systems

Articles of journal № 2 at 2022 year.

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11. Automated detection and classification of objects in the traffic flow on satellite images of the city [№2 за 2022 год]
Author: V.S. Tormozov
Visitors: 2475
The paper discusses the developed methods of detecting and classifying objects in a traffic flow on ul-tra-high spatial resolution space survey data. Due to appearing the large amounts of free access satellite data, the development of machine learn-ing methods based on geospatial data, in particular satellite data, is becoming increasingly urgent. The paper justifies the choice of a source of data on traffic flows – ultra-high resolution satellite images. It also describes the main problems and tasks associated with the recognition and classification of objects in traffic flows. The purpose of scientific work is to develop and study a chain of algorithms that allows detecting and classifying objects in traffic flows with high accuracy. The research is based on a numerical as-sessment of the quality of the algorithms. The work uses the methods of pattern recognition, machine learning and digital image processing. The scientific novelty of the completed work is based on: a unique algorithm for extracting images of local sections of the road network, an algorithm for determining the direction of object’s road movement, modernization of the selective search algorithm, which consists in filtering the extracted candidates. The work novelty is also confirmed by the fact that the used ultra-high resolution survey data have become accessible for private use relatively recently.

12. Development of a prototype solver for extended step theories of propositional logic [№2 за 2022 год]
Authors: Fominykh I.B., Alekseev N.P., N.A. Gulyakina, Kravchenko K.S., Fomina M.V.
Visitors: 2763
Nowadays, there are active researches on the possibilities of using non-classical logics in modeling the cognitive agent’s reasoning. The paper considers the problem of developing and implementing a prototype of an Extended Step Theory solver (EST) in the case when decisions on managing a complex technical object are made un-der strict time constraints. The authors consider a logical system based on using step theories with two types of negation, such systems are called EST. The use of two types of negation allows deducing both unbiased facts and belief facts, which is important when modeling human reasoning. The paper focuses on the issue of organizing the inference procedure based on using non-classical logics in modeling the reasoning of a cognitive agent. There are the main stages of the development of the EST prototype using the propositional logic lit-erals given. There are also descriptions for each solver component, its functions, tasks, input and out-put data. The authors is justify the choice of the clingo output system supporting the formation of ex-tended logic programs Answer Set Programming (ASP) as a tool for implementing the solver. The paper gives the algorithms of translating the EST into a logical program corresponding to the ASP syntax. When organizing logical inference, the authors used the algorithm of cyclic processing of EST belief sets in the clingo environment. The main stages of this algorithm are considered by an example that analyzes the solver’s operation stages and the presents the results in the clingo syntax. An example of the solver's work demonstrates the main EST features in hard real-time problems, such as the rejection of logical omniscience, self-knowledge and temporal sensitivity. It is planned further to consider the applicability of the created solver to a more complex formal system – the logic of first-order predicates.

13. Automating the assessment of the power grid state in remote areas of Russia using smart structures [№2 за 2022 год]
Author: Shevnina Yu.S.
Visitors: 2073
The paper discusses a method for automating the assessment of the power grid state in remote regions of Russia using smart structures. The proposed automation method is implemented as a mobile applica-tion. The smart structure underlying the described method of automating the assessment of the power grid state consists of modules for receiving and processing data from sensors, searching for patterns in the power grid characteristics and generating state classifiers, offering recommendations for repair and optimal operation of the power grid and substation. The scientific novelty of the proposed solution is in the method of analyzing and processing the power grid characteristics and their combinations. In addition, the external influence parameters in the form of natural and man-made factors are taken into account. The method of analyzing and processing information about the power grid and substation is based on the machine learning method – logical data analysis. Assessing the state of a power grid and a substation is important when studying and solving the problems of predicting changes in the power grid state, selecting recommendations and making de-cisions on repair and maintenance work. The method for assessing the power grid state is based on the search for patterns and the construc-tion of classifiers. It allows taking into account all the characteristics and parameters of a power grid, their totality and the relationship between them. In addition, the described method allows analyzing and obtaining patterns for incomplete and inaccurate data, which is a fairly common occurrence in real power networks. The method can be used in the design and maintenance of power grids and substations in hard-to-reach and remote regions of the Russian Federation. The proposed reduction of the characteristic regularities and their sets based on their recurrent con-junction makes it possible to obtain optimal classifiers of the states of a power grid and a substation with high interpretability and generalization. It increases the accuracy of assessing the power grid state, therefore, increases the accuracy of predicting behavior, recommendations and making decisions about repair work and the optimal mode operation.

14. A group multicriteria decision analysis module based on fuzzy extension of TOPSIS method [№2 за 2022 год]
Authors: Shershnev R.V., A.V. Radaev, Korobov A.V., Yatsalo B.I.
Visitors: 2966
The theory of group decision making is widely studied and applied in various fields of human activity. The theory of group decision making proposes various voting methods, assessing the consensus among the participants in the group analysis of decisions and recommendations for choosing/ranking alterna-tives. Different computer systems are developed to implement the process of group analysis and deci-sion support for practical applications. The paper presents the DecernsFMCDA-G-FT framework for group multicriteria decision analysis based on the fuzzy TOPSIS model. The framework is a component of the group decision support system DecernsFMCDA-G under development. The system provides the necessary functionality to define a problem, collect expert information, visualize individual and group preferences, rank alternatives, ana-lyze the results. Visualization of individual preferences, group assessments and the possibility of choosing different approaches for ranking the alternatives give a visual representation of the process of group multicriteria analysis. When solving applied problems, input fuzzy quantities of various shapes, several methods for cal-culating functions of fuzzy numbers as well as various methods for ranking fuzzy quantities can be used. The problem of multicriteria sorting candidates for employment is solved by using the Decerns-FMCDA-G-FT framework. The developed module is intended for study of decision theory within universities’ courses, risk analysis and management and for multicriteria analysis of a wide range of scientific and applied problems.

15. Software implementation of demographic data analysis based on the unified population register [№2 за 2022 год]
Authors: Yusifov F.F., Akhundova N.E.
Visitors: 1704
A unified population register is a key component of the e-demographic system. The register is based on the integrated databases exchanging both aggregated data and individual data between separate regis-ters. The paper examines the analysis of demographic data on the basis of a unified population register. Population registers play an important role in obtaining information about the population. It should be noted that the COVID-19 pandemic has once again emphasized the importance of using administrative data as e-registers for demographic research. The paper provides an experimental analy-sis of demographic characteristics in the context of the COVID-19 pandemic based on the data of indi-viduals integrated into a unified register. The data on individuals in the study are hypothetical data tak-en from two separate registers: the population and health registers. A database was taken for 1000 peo-ple integrated into the unified register. The paper presents the program implementation of demographic data analysis. Demographic analy-sis was implemented in Jupyter Notebook 6.1.4., Python 3.8.5. The results show that the establishment of an e-demographic system requires the integration of various state registers for more detailed analy-sis. This will allow processing and analyzing larger and more multidimensional structured data at dif-ferent time intervals. At the same time, the reliability of the information included in the register, the elimination of inconsistencies, and ensuring continuous updating of registration information for each individual are very important issues. Elimination of errors in registration data makes unified popula-tion registers a reliable source of information.

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